SALICON-SAL-SCE: We select a subset of images from the SALICON dataset and annotate each image with a scene category label based on the categories of the Place-CNN dataset [1]}. We removed images belonging to the scene categories with fewer than 50 images. Eventually, we are left with 6,107 images with both fixation maps and scene category labels (26 categories in total, see supplement). We use this database to compare the effect of saliency prediction and scene recognition on learned inner representations.
